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开发一种可解释的QSAR模型,用于预测基于GA-MLR算法在老鼠中激烈口服有机酸盐的毒性

Guanqi Yu^1,2, Qianlan Zhuo², Chuan Wang^2,3

¹School of Environmental Science & Engineering, Tianjin University, Tianjin, China.

Journal of environmental science and health. Part. B, Pesticides, food contaminants, and agricultural wastes

|April 17, 2025

概括

本研究引入了一种定量结构-活性关系 (QSAR) 模型来预测有机 (OP) 毒性,为传统方法提供了更快的替代方案. 该模型准确地确定了影响OP毒性的关键分子特征.

科学领域:

毒理学毒理学毒理学
计算化学计算化学
药用化学医学化学

背景情况:

有机酸盐 (OPs) 是具有广泛毒性的危险化学物质.
传统的体内毒性检测是耗时和劳动密集的.
预测模型的开发对于有效的风险评估至关重要.

研究的目的:

开发和优化定量结构-活性关系 (QSAR) 模型,用于预测有机酸盐 (OPs) 对老鼠的急性毒性.
确定影响OP毒性的关键分子描述因素.
为传统的毒性测试方法提供可靠和有效的替代方案.

主要方法:

利用二维分子和量子化学描述器用于QSAR建模.
采用基于遗传算法的多重线性回归 (GA-MLR) 来进行模型优化.
通过离开一次的交叉验证 (LOOCV) 和外部测试套件验证了模型.

主要成果:

最优的QSAR模型实现了0.7451的确定系数 (R^2) 和0.6208.8的交叉验证系数 (Q^2_Loo).
外部验证给出了0.7360的R^2_ext,证明了出色的预测性能.
NumHDonors和PEOE_VSA被确定为影响OP毒性的最重要的描述因素.

结论:

开发的QSAR模型提供了一种可靠和准确的方法来预测OP毒性.
与键捐赠和部分电荷相关的分子描述器显著影响OP毒性.
这种方法为风险评估和了解OP毒性机制提供了有价值的工具.

关键词:

有机酸盐有机酸盐在QSAR中使用QSAR.剧烈毒性的急性毒性机器学习是机器学习.分子描述器分子描述器多重线性回归的多重线性回归.

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